Generative Ai For Data Engineering Training Course: Ai-powered Data Pipelines

Introduction

Revolutionize your data engineering workflows with our Generative AI for Data Engineering Training Course. This program is designed to equip you with the essential skills to use generative AI to assist in data engineering tasks, enabling you to automate and optimize data pipelines with cutting-edge AI techniques. In today's rapidly evolving data landscape, mastering generative AI for data engineering is crucial for organizations seeking to enhance efficiency and innovation. Our generative AI training course offers hands-on experience and expert guidance, empowering you to leverage AI to transform data engineering practices.

This AI-powered data pipelines training delves into the core concepts of generative AI in data engineering, covering topics such as data synthesis, automated code generation, and AI-driven data quality checks. You'll gain expertise in using industry-standard tools and techniques to use generative AI to assist in data engineering tasks, meeting the demands of modern data-intensive organizations. Whether you're a data engineer, AI engineer, or data scientist, this Generative AI for Data Engineering course will empower you to build and manage intelligent data systems.

Target Audience:

• Data Engineers
• AI Engineers
• Data Scientists
• Machine Learning Engineers
• Software Developers
• Data Architects
• Anyone needing generative AI for data engineering skills

Course Objectives:

• Understand the fundamentals of generative AI for data engineering.
• Master data synthesis and augmentation using generative models.
• Utilize AI for automated code generation and data pipeline development.
• Implement AI-driven data quality checks and anomaly detection.
• Design and build intelligent data transformation workflows.
• Optimize data pipelines with AI-powered automation.
• Troubleshoot and address common issues in AI-assisted data engineering.
• Implement AI-driven metadata management and data discovery.
• Integrate generative AI with various data engineering tools and platforms.
• Understand how to handle large-scale AI-assisted data processing.
• Explore advanced generative AI techniques for data engineering (e.g., reinforcement learning for pipeline optimization).
• Apply real world use cases for generative AI in data engineering tasks.
• Leverage generative AI tools and frameworks for efficient data engineering.

Duration

10 Days

Course content

Module 1: Introduction to Generative AI for Data Engineering

• Fundamentals of generative AI for data engineering.
• Overview of data synthesis, code generation, and AI-driven quality checks.
• Setting up a generative AI development environment.
• Introduction to generative AI tools and frameworks.
• Best practices for AI-assisted data engineering.

Module 2: Data Synthesis and Augmentation

• Mastering data synthesis and augmentation using generative models.
• Utilizing generative adversarial networks (GANs) and variational autoencoders (VAEs).
• Implementing synthetic data generation for testing and training.
• Designing and building data augmentation pipelines.
• Best practices for data synthesis.

Module 3: Automated Code Generation

• Utilizing AI for automated code generation and data pipeline development.
• Implementing AI-driven SQL and Python code generation.
• Designing and building automated pipeline generation tools.
• Optimizing code generation for efficiency.
• Best practices for code generation.

Module 4: AI-Driven Data Quality Checks

• Implementing AI-driven data quality checks and anomaly detection.
• Utilizing machine learning for data validation.
• Designing and building anomaly detection systems.
• Optimizing quality checks for data integrity.
• Best practices for data quality.

Module 5: Intelligent Data Transformation Workflows

• Designing and building intelligent data transformation workflows.
• Utilizing AI for data cleaning and preprocessing.
• Implementing automated data integration and ETL processes.
• Optimizing workflows for data accuracy.
• Best practices for data transformation.

Module 6: Pipeline Optimization with AI

• Optimizing data pipelines with AI-powered automation.
• Utilizing reinforcement learning for pipeline optimization.
• Implementing AI-driven resource management.
• Designing efficient AI-assisted pipelines.
• Best practices for pipeline optimization.

Module 7: Troubleshooting AI-Assisted Data Engineering

• Troubleshooting and addressing common issues in AI-assisted data engineering.
• Analyzing AI model errors and pipeline failures.
• Utilizing problem-solving techniques for resolution.
• Resolving common AI-driven data errors.
• Best practices for troubleshooting.

Module 8: AI-Driven Metadata Management

• Implementing AI-driven metadata management and data discovery.
• Utilizing natural language processing (NLP) for metadata extraction.
• Designing and building AI-driven data catalogs.
• Optimizing metadata for data discovery.
• Best practices for metadata management.

Module 9: Integration with Data Engineering Tools

• Integrating generative AI with various data engineering tools and platforms.
• Utilizing APIs and data connectors.
• Implementing AI-driven tasks within existing data pipelines.
• Optimizing integration for data processing.
• Best practices for integration.

Module 10: Large-Scale AI-Assisted Data Processing

• Understanding how to handle large-scale AI-assisted data processing.
• Utilizing distributed AI models for data processing.
• Implementing data sharding and parallel processing.
• Designing scalable AI-driven data architectures.
• Best practices for large scale data.

Module 11: Advanced Generative AI Techniques

• Exploring advanced generative AI techniques for data engineering (reinforcement learning for pipeline optimization).
• Utilizing reinforcement learning for pipeline resource allocation.
• Implementing advanced AI models for data synthesis.
• Designing and building advanced AI-driven solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing generative AI for synthetic data generation in testing.
• Utilizing AI for automated ETL pipeline generation.
• Implementing AI-driven data quality monitoring in real-time.
• Utilizing AI for metadata enrichment and data discovery.
• Best practices for real-world applications.

Module 13: Generative AI Tools Implementation

• Utilizing generative AI tools and frameworks (TensorFlow, PyTorch).
• Implementing AI-driven pipelines with specific tools.
• Designing and building automated workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: AI-Driven Pipeline Monitoring

• Implementing AI-driven pipeline monitoring.
• Utilizing AI for anomaly detection in pipeline performance.
• Designing and building performance dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Generative AI for Data Engineering

• Emerging trends in generative AI for data engineering.
• Utilizing AI for automated data mesh implementation.
• Implementing AI for data governance and compliance.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.